Initial exploratory data analysis of ACLED-UCDP conflict trends

Comparison

Fatalities

Both ACLED and UCDP data on fatalities are quite highly correlated – in the cases in which there is overlap in reporting.

Of the 10 countries in the Asia-Pacific between 2014 and 2023 for which the data was comparable, 8 had a statistically significant relationship between the number of fatalities reported in the ACLED and UCDP datasets:

However, we note that the number of conflict fatalities reported by UCDP are consistently lower, due to a number of factors, which we will discuss later.

However, this finding illustrates our hypothesis that even when accounting for ACLED’s broader coverage of event types (protests), UCDP’s coverage of conflicts in the Asia-Pacific is not as comprehensive as that of ACLED.

We don’t really have enough for trends yet, but we can note prominent spikes in fatalities. For instance, Afghanistan had a fairly long lead up to the peak of violence in 2021, the US withdrawal and the subsequent lessening of tensions. Iran saw a spike of violence coinciding with the 2021-2022 Iranian protests. In Thailand, we see a lessening of tensions from the 2014 coup and its fallout, and the state reasserting itself. And the spike in Sri Lanka in 2019 was related to the 2019 Easter Sunday bombings.

Sources

We note that ACLED, in general, draws from a much larger number of sources than UCDP. This is true unless ACLED is not collecting a country’s data for a given year.

From the treemaps below, we see that not only does ACLED use more sources than UCDP, UCDP is also highly reliant on a relatively small number of sources: the top 10 sources in the UCDP dataset are responsible for 70.68% of all source articles.

Comparatively, the ACLED dataset is much more diverse. The top 10 sources in ACLED issued 25.24% of all source articles.

Types of violence

ACLED is an event-based dataset, as such, comparing event types does not really work out. Instead, we will undertake the comparison using interaction type, which is analogous to UCDP’s type of violence. variable.

UCDP and ACLED interaction and fatalities, 2014-2023

GED_type	ACLED_type	ACLED_fatalities
State-Based	State Forces Versus Rebels	129,979
State-Based	State Forces Versus Political Militia	40,846
State-Based	State Forces Versus Rioters	2,871
State-Based	Other	2,544
One-Sided	Political Militia Versus Civilians	19,185
One-Sided	State Forces Versus Civilians	17,887
One-Sided	Rebels Versus Civilians	9,276
One-Sided	Other	3,228
One-Sided	Rioters Versus Civilians	2,298
One-Sided	State Forces Versus Protesters	631
One-Sided	Sole Rioter Action	11
Non-State	Other	10,244
Non-State	Rioters Versus Rioters	2,309

Overall, there is just much less detail in the UCDP dataset. The map below mirrors another done by Raleigh, Kishi and Linke where they noted that UCDP did not collect sufficient data on incidents related to the Philippine drug war and the surrounding political violence.

The ACLED map below already excludes protest events. With the ACLED data, the linear patterns of violence become very clear, as various armed groups fought for the control of major roads and highways. ACLED also picked up on the violence around the borders with India and China, something which UCDP did not.

Actors

Perhaps one of the most easily identifiable differences is that conflict actors within the ACLED dataset are classified into broader groups, whereas UCDP data is not, preventing plots like these:

From the plot above, we observe that India, Pakistan, Indonesia, Bangladesh and Papua New Guinea all experience spikes in the activity of identity militias – this group includes a large number of tribal and communal militias.

In Thailand, China, Cambodia and the Philippines, the predominance of state actors indicate higher levels of state violence. In comparison, South Korea’s uptick in protest activity was not accompanied by an increase in state violence.

Myanmar is in open civil war and has seen a massive proliferation of political militias, who, in many cases have broader aims compared to identity militias. Let’s take a closer look and put the y-axis on a log scale so it’s easier to interpret: